Preparation Circuits for Matrix Product States by Classical Variational Disentanglement

[2504.21298] Preparation Circuits for Matrix Product States by Classical Variational Disentanglement Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2504.21298 (quant-ph) [Submitted on 30 Apr 2025 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: Preparation Circuits for Matrix Product States by Classical Variational Disentanglement Authors: Refik Mansuroglu , Norbert Schuch View a PDF of the paper titled Preparation Circuits for Matrix Product States by Classical Variational Disentanglement, by Refik Mansuroglu and Norbert Schuch View PDF HTML (experimental) Abstract: We study the classical compilation of quantum circuits for the preparation of matrix product states (MPS), which are quantum states of low entanglement with an efficient classical description. Our algorithm represents a near-term alternative to previous sequential approaches by reverse application of a disentangler, which can be found by minimizing bipartite entanglement measures after the application of a layer of parameterized disentangling gates. Since a successful disentangler is expected to decrease the bond dimension on average, such a layer-by-layer optimization remains classically efficient even for deep circuits. Additionally, as the Schmidt coefficients of all bonds are locally accessible through the canonical $\Gamma$-$\Lambda$ form of an MPS, the optimization algorithm can be heavily parallelized. We discuss guarantees and limitations to trainability and show numerical results for ground states of one-dimensional, local Hamiltonians as well as artificially spread out entanglement among multiple qubits using error correcting codes. Comments: 10+6 pages, 5+1 Figures, version essentially identical to the one published in PRA Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2504.21298 [quant-ph] (or arXiv:2504.21298v3 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2504.21298 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Phys. Rev. A 113, 042430 (2026) Related DOI : https://doi.org/10.1103/7x2g-twkl Focus to learn more DOI(s) linking to related resources Submission history From: Refik Mansuroglu [ view email ] [v1] Wed, 30 Apr 2025 04:13:01 UTC (441 KB) [v2] Sun, 16 Nov 2025 21:20:49 UTC (437 KB) [v3] Tue, 14 Apr 2026 07:55:41 UTC (303 KB) Full-text links: Access Paper: View a PDF of the paper titled Preparation Circuits for Matrix Product States by Classical Variational Disentanglement, by Refik Mansuroglu and Norbert Schuch View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2025-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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